Sampling and follow-up sheeme

Sampling

The sampling method used was density sampling. This plot shown a time scheme of the recruitment of cases with an example of 5 pairs of cases. Incident Diabetes cases were selected as they occurred and their respective controls of the same sex, age on inclusion date.

A case is a person who is recorded as diabetes at index date. A control is a person who has not yet have diabetes at the index date of the case. The index date of the case can also be the onset of a disease (Diabetes), in which case controls are persons who do not yet have the disease (are not yet exposed).

Follow-up

Once identified, they are followed until the end of follow-up. Which determines your time at risk (Black line). The follow-up was until 12/31/2018, until death or until switch group

Switch group

Example of change of group


Flow-Chart

Flow-Chart

Flow chart of how the sample finally analyzed has been arrived at. Initially we started from a potential population from Barcelona of 85,919, of which 10,065 Diabetics (Prevalent as incidents) + 75,854 potential controls. Of these, 8004 diabetics and 8004 controls of the same age and sex were matched.

Incidence

Incidence rates

During the follow-up, we collected data on Tuberculosis event recorded.

Number of subjects, tuberculosis events, and incidence rate (per 100.000 persons-year) and confidence interval (95%CI), Overall, by groups and type of diabetes during the follow-up

Group Subjects (N) Persons-years Tuberculosis events Incidence rate (95% CI)
Overall 16008 129804 73 56.2 (44.1;70.7)
Control 8004 61198 25 40.9 (26.4;60.3)
Diabetes 8004 68605 48 70.0 (51.6;92.8)
DM1 355 3505 2 57.1 (6.9;206.1)
DM2 7649 65101 46 70.7 (51.7;94.3)
DM Incident 3641 22965 23 100.2 (63.5;150.3)
DM Prevalent 4363 45640 25 54.8 (35.4;80.9)

Cumulative incidence curve by group

Sensitivity analysis

Sensitivity analysis

Additional analyses in order to check the robustness of the results include the different approach models

Model specifications
Model Adjust variables
1 Origin
2 Origin, Visits
3 Origin, CKD, BMI, Visits
4 Origin, Alcholism, Smoke, CKD, BMI, Visits
Methods

Competing risk: Competing Risks Regression for Clustered Data. Regression modeling of subdistribution hazards for clustered right censored data. Failure times within the same cluster are dependent. crrs R package Vesion 1.1. This method extends Fine-Gray proportional hazards model for subdistribution (1999) to accommodate situations where the failure times within a cluster might be correlated since the study subjects from the same cluster share common factors This model directly assesses the effect of covariates on the subdistribution of a particular type of failure in a competing risks setting.

Bingqing Zhou and Aurelien Latouche (2013). crrSC: Competing risks regression for Stratified and Clustered data. R package version 1.1. https://CRAN.R-project.org/package=crrSC

Cox PH by clusters: Fits a Cox proportional hazards regression model with clusters. coxph function from {survival} R packages Therneau T (2015). A Package for Survival Analysis in S. version 2.38, <URL: https://CRAN.R-project.org/package=survival>.

Full model

Full model

About

Authors and affiliations

Authors: Antonio-Arques, Violeta (VA); Franch-Nadal, Josep (JF); Moreno Martinez, Antonio (AM); Real Gatius, Jordi (JR); Orcau Palau, Àngels (AO); Caylà, Joan A (JC)

Affiliations

  • DAP-Cat group, Unitat de Suport a la Recerca Barcelona, Fundació Institut Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAP Jordi Gol), Barcelona, Spain;
  • CIBER of Diabetes and Associated Metabolic Diseases (CIBERDEM), Instituto de Salud Carlos III (ISCIII), Spain
  • Primary Health Care Center Raval Sud, Gerència d’Atenció Primaria, Institut Català de la Salut, Barcelona, Spain

Correspondence: ;

Author Contributions: Conceptualisation: AM, AO and CJ; methodology and design: JR JF; formal analysis: JR; resources and data curation: VA and JR.; writing—original draft preparation. VA, JC,AM ; writing—review and editing: VA, JF, JR, AM; supervision: JF and VA; project administration. JF and AO; funding acquisition: AM